Method of estimating expected revenues from business directory books

ABSTRACT

A new system of analyzing advertising revenue derivation is provided that is particularly useful for predicting an amount of revenue that can be expected from advertisements placed in a business directory book having a certain geographic coverage. The system comprises estimating a preference factor of advertisers located in a first geographic area of placing advertisements in other geographic areas. This preference factor, along with other demographic data, is used to calculate a choice probability that advertisers will choose to place an advertisement in one business directory book over other competing books. A total amount of expected revenue for each particular geographic area within a metropolitan area is calculated. Finally, the total amount of expected revenue attributable to a particular business directory book is predicted based on the choice probability and the total amount of expected revenue for each geographic area.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a Divisional application to U.S. patentapplication Ser. No. 09/798,336, filed Mar. 2, 2001 now U.S. Pat. No.7,039,598, the entirety of which is incorporated by reference herein.

FIELD OF THE INVENTION

The present invention relates generally to a system of analyzingadvertising publications. More particularly, the invention relates to asystem of predicting revenues expected to be derived from a businessdirectory book, commonly known as a yellow pages book, having aparticular geographic coverage.

BACKGROUND OF THE INVENTION

Throughout metropolitan areas, multiple business directories, normallypublished by different publishers and commonly known as “yellow pages”books, compete for the business of the various advertisers in the area.The competing yellow pages books generally have different, butoverlapping geographic coverages. In other words, in a givenmetropolitan area, various yellow pages books are distributed todifferent, overlapping subsets of the entire metropolitan area. Forexample, one yellow pages book may be distributed to suburbs A, B, andC, whereas a different yellow pages book may be distributed to suburbsB, C, D, and E. It is common that the various suburbs have differentdemographic characteristics in terms of business activity, consumerpopulation, aggregate household income, and the like.

Businesses that advertise in yellow pages books tend to choose from themultiple competing yellow pages books, or at least allocate differentlevels of advertising dollars to the various competing books. Businessesgenerally desire to be listed in yellow pages books that are distributedto the households that immediately surround it. However, there may bemultiple books that meet that criteria, each with a somewhat differentcoverage. So, businesses tend to either choose one of the books orallocate different levels of advertising dollars among the bookscovering its area. Furthermore, businesses may desire to place yellowpages advertisements that reach more distant geographic areas within thesame general metropolitan area. Similarly, some geographic areas arehighly desirable in which to place advertisements, usually because ofthe particular demographics of that area. As a result, the specificgeographic coverage of a yellow pages book (i.e., households to which abook is distributed) is important to the amount of advertising revenuethat the book attracts, and thus, the ultimate financial success of thatbook.

Typically, one of the first steps in setting the scope of a new yellowpages book involves hiring a market research firm to survey themarketplace, provide data and to make predictions relating to thepotential derivable advertising revenue and market share of a proposedyellow pages book. The market research process is typically long andexpensive, and includes implementing consumer and business surveys thatare custom-designed for each metropolitan area. The survey results mustbe analyzed by market research professionals to provide market sharepredictions. The entire market research process typically takes severalmonths to complete. Further, because the market research process isspecific to each individual metropolitan area, the entire marketresearch process must be started from scratch each time a publisherdesigns a new book in a different metropolitan area. Even after themarket research is complete, providing feedback for books of variouscoverages requires several steps and is not instantaneous. As a result,the process of analyzing several proposed books of varying coverages todetermine which coverage provides the greatest expected advertisingrevenue can be an arduous one.

Therefore, a need exists for an improved method of predicting expectedrevenue generation of yellow pages books having certain geographiccoverages.

SUMMARY OF THE INVENTION

Embodiments of the present invention relate to a method of analyzinggeographic advertising preferences of potential advertisers, wherein anumerical preference factor indicative of a relative preference of thepotential advertisers located in a first geographic area of placingadvertisements in a second geographic area is determined.

Accordingly, the present invention can be embodied as an improved systemof analyzing yellow pages books, and, in particular, predicting expectedrevenue generation of proposed yellow pages books having certaingeographic coverages. For purposes of the invention, a given geographicarea, typically a metropolitan area, is conceptually subdivided intoseveral geographic “cells.” Each cell has its own set of demographiccharacteristics, including such things as population, aggregatehousehold income, number of non-manufacturing businesses, total sales ofsuch businesses, etc. The invented system comprises using threepredictive mathematical models to predict the total expected advertisingrevenue from the proposed book based on (i) demographic information ofthe various cells, and (ii) information about a proposed book andcompeting books.

First, a “cell preference model” is used to estimate the aggregatepreference of advertisers located in one geographic cell to have theiryellow pages advertisements reach customers in each of the other cellsin the metropolitan area. The output of the cell preferences model,i.e., a cell preference factor, is used in a “book choice model” toestimate the probability of an advertiser located in one cell ofadvertising in one particular book over the other competing books. Then,cell demographic information is used by a “revenue model” to determinethe total advertising revenue that is expected to be derived from eachof the geographic cells. Finally, the total expected advertising revenuefrom each cell is multiplied by the probability that advertisers fromvarious cells will advertise in each of the books. The ultimate resultis a predicted total revenue that is expected to be derived from ayellow pages book having a certain coverage in a given metropolitanarea.

According to one aspect of the invention, a method analyzing geographicadvertising preferences of potential advertisers is provided, comprisingthe step of determining a numerical preference factor indicative of arelative preference of the potential advertisers located in a firstgeographic area of placing advertisements in a second geographic area.

According to a second aspect of the invention, a method of predictingadvertising revenue derived from a business directory book is provided,comprising the steps of estimating a geographic preference ofadvertisers located in a first geographic area of placing advertisementsin a second geographic area; and determining an expected amount ofadvertising revenue attributable to the business directory book based onsaid geographic preference.

According to a third aspect of the invention, a system for evaluatingexpected revenues derived from business directory books is provided,comprising a cell preference model that determines a value indicative ofthe preference of advertisers located in a first cell to advertise in asecond cell; a book choice model responsive to said advertiserpreference that determines a probability that an advertiser located insaid first cell will choose to advertise in a business directory bookhaving certain characteristics; and a revenue model that estimates totaladvertising revenue potentials respectively of said first and secondcells.

The invented system is superior to known systems of analyzing proposedyellow pages books in at least the following ways. First, the system ofthe present invention is transportable between markets. For example, theinvented system can be used in Chicago in the same manner that it can beused in Dallas. The only difference between the applications is theinput data, i.e., the demographic data and the information about theproposed and competing books. The input data is generally readilyavailable from public information sources, such as census data. Second,the inventive system is readily adaptable to be implemented in softwareon a computer. As a result, input information regarding the scope of theproposed yellow pages book can be easily changed and the new expectedrevenue is returned very quickly. Therefore, the invented system caneasily be used to analyze several proposed coverages quickly, thusenabling improved revenue optimization. Finally, the invented systemeliminates the need to design custom surveys for each new market andspend several months obtaining and analyzing market research data. As aresult, the design of new yellow pages books can be performed morequickly and less expensively than known in prior practice.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a relational flowchart that illustrates a preferred embodimentof the invention, including the relationship between the differentpredictive models and the input data.

DETAILED DESCRIPTION OF A PREFERRED EMBODIMENT OF THE INVENTION

The invented methodology comprises a system for predictingbusiness-advertising activity in various competing business directorybooks, commonly known as “yellow pages” books. For a given geographicarea, the invented system predicts which yellow pages book businessesare likely to advertise in each available yellow pages book as well aspredicting the amount of revenue that such businesses will spendadvertising in a particular book.

The invented system is premised on the notion that businesses indifferent metropolitan areas will behave similarly when choosing whichamong the various competing yellow pages books in their own geographicarea to advertise in. For purposes of this system, a given metropolitanarea is broken down into a number of geographic “cells”. Each cell hasits own set of characteristics, including such things as number ofhouseholds, aggregate household income and the number ofnon-manufacturing businesses. Commonly, each particular yellow pagesbook is distributed to a number of geographic cells. The invented systemis used to predict the amount of revenue that a yellow pages book willgenerate in light of the particular geographic cells to which it isdistributed.

From a macro perspective, the invented system can be broken down intothree different predictive mathematical models that work together toultimately predict the total amount of revenue that businesses willspend advertising in a given yellow pages book having a particulargeographic coverage. With reference to FIG. 1, the invented systemincludes a “cell preference model” (block 18), a “book choice model”(block 20) and a “revenue model” (block 22). These three models dependon input data received from two basic data tables: (i) a “cell table”12, and (ii) a “book table” 14. The cell table 12 includes data abouteach geographic cell, including number of household, aggregate householdincome, number of non-manufacturing businesses, identity of the localtelephone service provider and geographic information (to calculatedistance between cells). The book table 14 has data relating to each ofthe competing yellow pages books, including, for example, data relatingto which cells are covered by which books.

The three predictive models will be explained in detail below. However,generally, the cell preference model 18 is used to compute a cellpreference factor, p_(ji), which is indicative of the relativepreference (p) of a business located in cell i to advertise in cell j.To make this prediction, the cell preference model depends on thephysical distance between the cells (D_(ji)), the aggregate householdincome in cell j (I_(j)) and the total sales of the businesses locatedin cell j (B_(j)). The cell preference factor is used in a book choicemodel 20.

The book choice model 20 computes a book attractiveness factor, s_(ki),which is indicative of the attractiveness of a given yellow pages bookrelative to other competing yellow pages books. The book attractivenessfactor depends on, among other things, the cell preference factor (fromthe cell preference model 18), the particular cells covered by theyellow pages book, and the aggregate income of households within thecoverage of the book. The book attractiveness factor is used to computethe probability that advertisers in a given cell will advertise in aparticular book. Finally, a revenue model 22 predicts the revenuepotential of a particular geographic cell. The revenue model depends oninput data in accordance with specific factors including, for example,the number of non-manufacturing businesses in the cell and the totalsales of non-manufacturing businesses in the cell. Ultimately, the totalamount of revenue generation for a given yellow pages book (with aparticular geographic scope) is calculated (block 24) based on thechoice probability (from the book choice model) and the revenuepotential of each covered cell (from the revenue model 22).

Now, the invented system will be described in more detail, withparticular attention to each of the predictive models. The cellpreference model 18 is designed to explain advertisers' preferences andpredict their preferences for advertising in the various geographiccells. The cell preference model 18 computes a cell preference factor(p_(ij)) indicative of the preference of advertisers located in cell ito advertise in cell j. The cell preference model 18 is as follows:ρ _(ji)=β_(o)+β_(D)1n(D _(ji))+β_(I) I _(j)+β_(B) B _(j)where:

D_(ji) is the distance (in miles) from cell k to cell j;

I_(j) is the aggregate income (in thousands of dollars) of allhouseholds located in cell j;

B_(j) is the total sales (in millions of dollars) of all businesseslocated in cell j.

The estimated coefficients may be empirically determined based on surveydata using the GLM procedure (generalized least squares regression)contained in the SAS (SAS Institute, Inc.) suite of data analysisprocedures. The coefficients are as follows:

Coefficient Value β₀  1.1933 β_(D) −0.4035 β_(I)  1.087E−8 β_(B) 4.916E−6The input data, D (distance), I(aggregate income) and B (total sales)are read from the cell table 12, which is a database of informationrelated to each geographic cell in the metropolitan area. The aggregateincome and total sales data are input directly to the cell preferencemodel. The geographic data for each cell is used to calculate thedistance between the various geographic cells, as shown in block 10 (ofFIG. 1), prior to being input into the cell preference model.

For each geographic cell in a given metropolitan area, the cellpreference model 18 is used to compute multiple cell preference factors,one for each of the other cells in the metropolitan area. For example,if a given metropolitan area is divided into 100 cells, then 100 cellpreference factors are calculated for each cell. For cell 1, the 100cell preference factors (p_(1,1)-p_(1,100)) would be indicative of therelative preference that advertisers located in cells 1-100 have foradvertising in cell 1. Similarly, for cell 2, the 100 cell preferencefactors (p_(2,1)-p_(2,100)) would be indicative of the relativepreference that advertisers located in cells 1-100 have for advertisingin cell 2. As described below, these cell preference factors, p, areused in the book choice model 20 to calculate a book attractivenessscore, s, for each of the competing yellow pages books in the samemetropolitan area.

The book choice model 20 is a multinominal logit choice model that wasdeveloped based upon empirical research data from a sample metropolitanmarket. First, for each yellow pages book, k, in a metropolitan area,the book choice model 20 computes an attractiveness score, s_(ki), foradvertisers located in each geographic cell, i, of the metropolitanarea. The attractiveness score is indicative of how desirable anadvertiser located in a particular cell, i, finds a given book k havingcertain characteristics. The attractiveness score depends upon: (i) thepreference (from the cell preference model) of advertisers in cell i toadvertise in cell j; (ii) the set of cells covered by yellow pages bookk; (iii) the aggregate income of all households to which the yellowpages book k is distributed; and (iv) whether the local phone exchangecarrier publishes book k or not. For example, the attractiveness of agiven book k to an advertiser located in cell i increases with thepreference of advertisers to place advertisements in the cells coveredby book k. Similarly, the attractiveness of a given book k increaseswith the aggregate income of households covered by book k. Further, theattractiveness of a given book k has been found to be greater if it ispublished by the local telephone carrier. The attractiveness score isdetermined according to the following attractiveness equation:S _(ki)=Σ_(jεB(k))ρ_(ji)[1+β_(PA) A _(k)+β_(PL) LEC(k,i)]+β_(A) A _(k)where:

p_(ji) is the preference factor of advertisers located in cell i toplace advertisements in geographic cell j (from the cell preferencemodel);

B(k) is the set of geographic cells to which yellow pages book k isdistributed;

A_(k) is the aggregate income of all households to which yellow pagesbook k is distributed (in billions of dollars); and

LEC(k,i)—1 if the local telephone exchange carrier for cell i publishesbook k, and 0 otherwise.

The estimated coefficients (from empirical data) used in theattractiveness equation are:

Coefficient Value β_(PA) −0.0174 β_(PL) 0.1698 β_(A) 0.1080

As shown in FIG. 1, the input data to the attractiveness score equationare received from the cell preference model 18, the cell table 12, andthe book table 14. In particular, the preference factor p is derivedfrom the cell preference model 18. The other input data, B(k), A_(k),and LEC(k,i) are calculated (in block 16) based upon book and cell datastored in the book table 14 database and the cell table 12 database,respectively. The output of the attractiveness equation is a set ofattractiveness scores for each competing yellow pages book in themetropolitan area, wherein each attractiveness score in each set isindicative of the attractiveness of the given book k relative to each ofthe geographic cells i in the metropolitan area. For example, for agiven metropolitan area that is subdivided into 100 geographic cells,the attractiveness equation would be used to calculate 100attractiveness scores for each of the competing yellow pages books.

The attractiveness scores are then used in a multinominal logit choicemodel, which estimates the probability that an advertiser located in agiven geographic cell will choose to advertise in a particular book. Inother words, the book choice model 20 determines the probability P_(ik)that a randomly selected advertiser located in cell i will choose toadvertise in book k in light of the universe of n books in themetropolitan market. The book choice model 20 is given by:P _(ik) =Pr ob(i chooses k)=exp(θs _(ki))/[Σ_(j=1, . . . n)exp(θs_(kj))+1]The estimated value of θ (determined from empirical data) used in thebook choice model 20 is 0.7300. The denominator of the book choice model20 reflects a total of n+1 choices (instead of n). The additional choicerepresents a fictitious book with zero attractiveness. Therefore, if allthe books have small or negative attractiveness for advertisers in agiven geographic cell, then this fictitious book, will be chosen withhigh probability. The fictitious book represents the option of notadvertising in any book. Referring to FIG. 1, the probability factors P(derived in block 20) are used in combination with the output from thecell revenue model (block 22) to calculate the expected book revenues inblock 24. The coefficients of the attractiveness equation were estimatedsimultaneously with the coefficient * using the procedure in the LIMDEP(Econometric software Inc.) package of econometric software forestimating multinominal logit models. The data used were the actualadvertiser expenditures in each cell for yellow pages advertising ineach book in the market.

The cell revenue model 22 is a nonlinear regression model that predictsthe total revenue expended by all advertisers located in a metropolitanarea to place yellow pages advertisements in each of the geographiccells in the metropolitan area. In other words, the cell revenue model22 predicts the total advertising revenue potential of a particulargeographic cell. The output of the cell revenue model 22 depends on thenumber of non-manufacturing businesses (Ni) located in cell i and thetotal sales, in millions of dollars, (S_(i)), of non-manufacturingbusinesses located in cell i. The cell revenue model 22 is given by:R _(i)=ρ_(o)+ρ_(N) N _(i)+ρ_(NN) N ₁ ²−ρ_(NS) N _(i) S _(i)+ρ_(s) S _(i)The coefficients were estimated (from empirical data) using the LINESTregression function in EXCEL (Microsoft, Inc.), with the product andsquared terms calculated prior to the estimation. The data used were thetotal expenditures for yellow pages advertising in each cell. Theestimated values are:

Coefficient Value ρ₀ 328,115 ρ_(N) 527.06 ρ_(NN) 0.19223 ρ_(NS) 0.10647ρ_(S) 292.14The value R_(i) from the cell revenue model equation is the totalrevenue potential of cell i. The total cell revenue potential valuesR_(i) are then used with the probability factors P (from block 20 ofFIG. 1) to calculate the total expected book revenue for a given yellowpages book.

The final step of the invented system comprises calculating the totalexpected expenditures for advertisements in a given yellow pages book.First, the expected expenditures for yellow pages advertisements in agiven book k by businesses located in the given cell i are calculatedaccording to the following equation:R _(ik) =P _(ik) R _(i)Finally, as shown in book revenues 24, the total expected revenue BR_(k)for a given yellow pages book k derived from all advertisers in themetropolitan area is given as:BR _(k)=Σ_(i) R _(ik)=Σ_(i) P _(ik) R _(i)Thus, the value BR_(k) represents the total revenue that can be expectedto be derived from a particular yellow pages book having the givencoverage characteristics.

The invented system can be used to produce revenue predictions in avariety of metropolitan areas wherein the specific cell and book datarelating to those metropolitan areas were used as input data to themodels. The invented system can be implemented in a variety of ways, forexample, it can be implemented as an “add-in” tool for MapInfo™, acommercially-available geographical information system that is known tothose of skill in the art.

Preferred embodiments of the present invention have been disclosed. Aperson of ordinary skill in the art would realize, however, that certainmodifications would come within the teachings of this invention.Therefore, the following claims should be studied to determine the truescope and content of the invention.

1. A method of analyzing geographic advertising preferences of potentialadvertisers, comprising the steps of: determining a numeric cellpreference factor; and displaying the cell preference factor to indicatea preference of the potential advertisers located in a first geographicarea of placing advertisements in a second geographic area, the firstgeographic area and the second geographic area having a distance betweenthem.
 2. The method of claim 1, wherein said cell preference factor iscalculated in part based on the distance between said first geographicarea and said second geographic area.
 3. The method of claim 1, whereinsaid cell preference factor is calculated in part based on an aggregateincome of households located in said second geographic area.
 4. Themethod of claim 1, wherein said cell preference factor is calculated inpart based on total sales of non-manufacturing businesses located insaid second geographic area.
 5. The method of claim 1, wherein said cellpreference factor is determined using: (i) a distance between said firstgeographic area and said second geographic area; (ii) an aggregateincome of households located in said second geographic area; and (iii) atotal sales of non-manufacturing businesses located in said secondgeographic area.
 6. The method of claim 5 wherein said cell preferencefactor is calculated according to the following equation:P _(ji)=β_(o)+β_(D)1n(D)+β_(I) I+β _(B) B, where P_(ji) is said cellpreference factor; D is said distance between said first geographic areaand said second geographic area; I is said aggregate income ofhouseholds located in said second geographic area; B is said total salesof non-manufacturing businesses located in said second geographic area;and β_(O), β_(D), β_(I), and β_(B) are estimated constants.
 7. A methodof predicting advertising revenue derived from a business directorybook, comprising the steps of: estimating a numeric geographicpreference of advertisers located in a first geographic area of placingadvertisements in a second geographic area; determining an expectedamount of advertising revenue attributable to the business directorybook based on said geographic preference, the first geographic area andthe second geographic area having a distance between them; anddisplaying the expected amount of advertising revenue.
 8. The method ofclaim 7, further comprising the steps of: determining a choiceprobability factor indicative of the probability that advertiserslocated in said first geographic area will choose to advertise in thebusiness directory book; and deriving an expected total amount ofadvertising revenue from said first geographic area.
 9. The method ofclaim 8, wherein said step of determining a choice probability factorincludes said geographic preference.
 10. The method of claim 8, furthercomprising the step of determining a predicted advertising revenuederived from the business directory book based on said choiceprobability factor and said expected total amount of advertising revenuefrom said first geographic area.
 11. The method of claim 7, wherein saidgeographic preference is determined in part using the distance betweensaid first geographic area and said second geographic area.
 12. Themethod of claim 7, wherein said geographic preference is determined inpart using an aggregate income of households located in said secondgeographic area.
 13. The method of claim 7, wherein said geographicpreference is determined in part using a total sales ofnon-manufacturing businesses located in said second geographic area. 14.The method of claim 7, wherein said geographic preference is determinedusing: (i) the distance between said first geographic area and saidsecond geographic area; (ii) an aggregate income of households locatedin said second geographic area; and (iii) a total sales ofnon-manufacturing businesses located in said second geographic area. 15.A method of predicting advertising revenue derived from a businessdirectory book comprising the steps of: estimating a numeric geographicpreference of advertisers located in a first geographic area of placingadvertisements in a second geographic area, the first geographic areaand the second geographic area having a distance between them, whereinsaid geographic preference is determined using: (i) a distance betweensaid first geographic area and said second geographic area; (ii) anaggregate income of households located in said second geographic area;and (iii) a total sales of non-manufacturing businesses located in saidsecond geographic area; determining an expected amount of advertisingrevenue attributable to the business directory book based on saidgeographic preference; determining a choice probability factorindicative of the probability that advertisers located in said firstgeographic area will choose to advertise in the business directory book;deriving an expected tool amount of advertising revenue from said firstgeographic area; and displaying the total amount of advertising revenue.16. The method of claim 15 further comprising the step of determining apredicted advertising revenue derived from the business directory bookbased on said choice probability factor and said expected total amountof advertising revenue from said first geographic area.